Revealing topoclimatic heterogeneity using meteorological station data

Abstract

Climate is a crucial driver of the distributions and activity of multiple biotic and abiotic processes, and thus high‐quality and high–resolution climate data are often prerequisite in various environmental research. However, contemporary gridded climate products suffer critical problems mainly related to sub‐optimal pixel size and lack of local topography‐driven temperature heterogeneity. Here, by integrating meteorological station data, high‐quality terrain information and multivariate modelling, we aim to explicitly demonstrate this deficiency. Monthly average temperatures (1981–2010) from Finland, Sweden and Norway were modelled using generalized additive modelling under (1) a conventional (i.e. considering geographical location, elevation and water cover) and (2) a topoclimatic framework (i.e. also accounting for solar radiation and cold‐air pooling). The performance of the topoclimatic model was significantly higher than the conventional approach for most months, with bootstrapped mean R2 for the topoclimatic model varying from 0.88 (January) to 0.95 (October). The estimated effect of solar radiation was evident during summer, while cold air pooling was identified to improve local temperature estimates in winter. The topoclimatic modelling exposed a substantial temperature heterogeneity within coarser landscape units (>5 °C/1 km−2 in summer) thus unveiling a wide range of potential microclimatic conditions neglected by the conventional approach. Moreover, the topoclimatic model predictions revealed a pronounced asymmetry in average temperature conditions, causing isotherms during summer to differ several hundreds of metres in altitude between the equator and pole facing slopes. In contrast, cold‐air pooling in sheltered landscapes lowered the winter temperatures ca. 1.1 °C/100 m towards the local minimum altitude. Noteworthy, the analysis implies that conventional models produce biassed predictions of long‐term average temperature conditions, with errors likely to be high at sites associated with complex topography.

Monthly microclimate models in a managed boreal forest landscape

“…The effects of landscape physiography on near-ground temperatures, in terms of incoming solar radiation modified by slope and aspect, pooling of cold heavy air in depressions, adiabatic decrease in temperature towards higher elevations and the moderating influence of soil moisture, air humidity and water bodies have been well studied (Aalto et al, 2017;Dobrowski, 2011;Geiger et al, 2012;Meineri and Hylander, 2016). Vegetation, on the other hand, can have substantial effects on microclimate by canopy shading, evaporative cooling, reduced wind speed, resulting in reduced lateral transfer of humidity and heat, buffering against heat loss overnight and changes in absorbance of shortwave radiation by differences in albedo (Geiger et al, 2012;Rosenberg, 1974) -referred to as biophysical processes sensu Lenoir et al (2017).…”

Microclima: An r package for modelling meso‐ and microclimate

“…Overall, the predictive power of our models compare well with other more location‐specific models (Aalto, Riihimäki, Meineri, Hylander, & Luoto, ; Pike, Pepin, & Schaefer, ), and build on previous methods by presenting a method for capturing the effects of vegetation structure on microclimate (cf. Bennie et al., ) or by incorporating mesoclimatic processes (cf.…”

The accuracy of climate variability and trends across Arctic Fennoscandia in four reanalyses

“…The spatial scale of modern global reanalyses, although considerably improved over previous versions, is still too coarse to accurately represent such events. This is especially the case in regions of steep and complex orography, such as the Scandinavian Mountains, where both SAT and PPN can vary markedly over short distances (e.g., Johansson and Chen, ; Yang et al, ; Pike et al, ; Aalto et al, ). The examples when the interquartile ranges of SAT and/or PPN from the gridded reanalysis data and observations fail to even overlap provide clear evidence of the spatial mismatch between the two data sets.…”